Systems and methods for controlling sensing device field of view

ABSTRACT

Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: receiving a current position of the vehicle along a determined path; retrieving map information that includes a pitch and a curvature of a roadway at or near the current position; determining, based on the map information, a planned pitch and a planned roll of the vehicle at or near the current position; determining, based on the planned pitch and the planned roll, a location of the field of view of the sensing device; determining, based on the location of the field of view and a location of an area of interest, an amount of movement of the sensing device to align the field of view with the area of interest; and generating, one or more control signals to one or more actuators associated with the sensing device based on the determined amount of movement.

TECHNICAL FIELD

The present disclosure generally relates to autonomous vehicles, andmore particularly relates to systems and methods for controlling a fieldof view of a sensing device based on a planned path and a real-timemovement of the vehicle.

INTRODUCTION

An autonomous vehicle is a vehicle that is capable of sensing itsenvironment and navigating with little or no user input. An autonomousvehicle senses its environment using sensing devices such as radar,lidar, image sensors, and the like. The autonomous vehicle systemfurther uses information from global positioning systems (GPS)technology, navigation systems, vehicle-to-vehicle communication,vehicle-to-infrastructure technology, and/or drive-by-wire systems tonavigate the vehicle.

Vehicle automation has been categorized into numerical levels rangingfrom Zero, corresponding to no automation with full human control, toFive, corresponding to full automation with no human control. Variousautomated driver-assistance systems, such as cruise control, adaptivecruise control, and parking assistance systems correspond to lowerautomation levels, while true “driverless” vehicles correspond to higherautomation levels.

Generally, the sensing devices associated with the autonomous vehiclehave a field of view. The sensing device is capable of sensing theenvironment observed in its field of view. During the operation of theautonomous vehicle, a position of the sensing device may be controlledsuch that the field of view is aligned with an area of interest. Incertain instances, however, due to terrain (e.g. hills), unexpected roadconditions (e.g. potholes), the field of view of the sensing device maynot align with the area of interest.

Accordingly, it is desirable to provide systems and methods that controla field of view of a sensing device based on a planned path and areal-time movement of the vehicle such that the field of view of thesensing device maintains alignment with the area of interest.Furthermore, other desirable features and characteristics of the presentdisclosure will become apparent from the subsequent detailed descriptionand the appended claims, taken in conjunction with the accompanyingdrawings and the foregoing technical field and background.

SUMMARY

Systems and method are provided for controlling a field of view of asensing device of an autonomous vehicle. In one embodiment, a methodincludes: receiving a current position of the autonomous vehicle along adetermined path; retrieving map information that includes a pitch and acurvature of a roadway at or near the current position; determining, bya processor, based on the map information, a planned pitch of theautonomous vehicle at or near the current position; determining, by theprocessor, based on the map information, a planned roll of theautonomous vehicle at or near the current position; determining, by theprocessor, based on the planned pitch and the planned roll of theautonomous vehicle, a location of the field of view of the sensingdevice; determining, based on the location of the field of view and alocation of an area of interest at the current position, an amount ofmovement of the sensing device to align the field of view with the areaof interest; and generating, by the processor, one or more controlsignals to one or more actuators associated with the sensing device tomove the sensing device to align the field of view based on thedetermined amount of movement.

BRIEF DESCRIPTION OF THE DRAWINGS

The exemplary embodiments will hereinafter be described in conjunctionwith the following drawing figures, wherein like numerals denote likeelements, and wherein:

FIG. 1 is a functional block diagram illustrating an autonomous vehiclehaving a sensing device field of view control system, in accordance withvarious embodiments;

FIG. 1A is a schematic illustration of a sensing device for theautonomous vehicle of FIG. 1, which has a field of view that iscontrolled by the sensing device field of view control system of FIG. 1;

FIG. 2 is a functional block diagram illustrating a transportationsystem having one or more autonomous vehicles of FIG. 1, in accordancewith various embodiments;

FIGS. 3 and 4 are dataflow diagrams illustrating an autonomous drivingsystem that includes the sensing device field of view control system ofthe autonomous vehicle, in accordance with various embodiments; and

FIGS. 5-7 are flowcharts illustrating a control method for controlling afield of view of one or more sensing devices of the autonomous vehiclein accordance with various embodiments.

DETAILED DESCRIPTION

The following detailed description is merely exemplary in nature and isnot intended to limit the application and uses. Furthermore, there is nointention to be bound by any expressed or implied theory presented inthe preceding technical field, background, brief summary or thefollowing detailed description. As used herein, the term module refersto any hardware, software, firmware, electronic control component,processing logic, and/or processor device, individually or in anycombination, including without limitation: application specificintegrated circuit (ASIC), an electronic circuit, a processor (shared,dedicated, or group) and memory that executes one or more software orfirmware programs, a combinational logic circuit, and/or other suitablecomponents that provide the described functionality.

Embodiments of the present disclosure may be described herein in termsof functional and/or logical block components and various processingsteps. It should be appreciated that such block components may berealized by any number of hardware, software, and/or firmware componentsconfigured to perform the specified functions. For example, anembodiment of the present disclosure may employ various integratedcircuit components, e.g., memory elements, digital signal processingelements, logic elements, look-up tables, or the like, which may carryout a variety of functions under the control of one or moremicroprocessors or other control devices. In addition, those skilled inthe art will appreciate that embodiments of the present disclosure maybe practiced in conjunction with any number of systems, and that thesystems described herein is merely exemplary embodiments of the presentdisclosure.

For the sake of brevity, conventional techniques related to signalprocessing, data transmission, signaling, control, and other functionalaspects of the systems (and the individual operating components of thesystems) may not be described in detail herein. Furthermore, theconnecting lines shown in the various figures contained herein areintended to represent example functional relationships and/or physicalcouplings between the various elements. It should be noted that manyalternative or additional functional relationships or physicalconnections may be present in an embodiment of the present disclosure.

With reference to FIG. 1, a sensing device field of view control systemshown generally at 100 is associated with a vehicle 10 in accordancewith various embodiments. In general, the sensing device field of viewcontrol system 100 maintains a field of view of a sensing deviceassociated with the vehicle 10 in alignment with an area of interestduring a pitch and/or a roll of the vehicle 10 and intelligentlycontrols a positon of the sensing device based thereon. By intelligentlycontrolling the position of the sensing device during a pitch and/orroll of the vehicle 10, a sensing device may be employed that has anarrow field of view.

As depicted in FIG. 1, the vehicle 10 generally includes a chassis 12, abody 14, front wheels 16, and rear wheels 18. The vehicle 10 has avehicle coordinate system, which is a frame of reference for determininga location of an object on the vehicle 10. The body 14 is arranged onthe chassis 12 and substantially encloses components of the vehicle 10.The body 14 and the chassis 12 may jointly form a frame. The vehiclewheels 16-18 are each rotationally coupled to the chassis 12 near arespective corner of the body 14.

In various embodiments, the vehicle 10 is an autonomous vehicle and thesensing device field of view control system 100 is incorporated into theautonomous vehicle 10 (hereinafter referred to as the autonomous vehicle10). The autonomous vehicle 10 is, for example, a vehicle that isautomatically controlled to carry passengers from one location toanother. The vehicle 10 is depicted in the illustrated embodiment as apassenger car, but it should be appreciated that any other vehicleincluding motorcycles, trucks, sport utility vehicles (SUVs),recreational vehicles (RVs), marine vessels, aircraft, etc., can also beused. In an exemplary embodiment, the autonomous vehicle 10 is aso-called Level Four or Level Five automation system. A Level Foursystem indicates “high automation,” referring to the drivingmode-specific performance by an automated driving system of all aspectsof the dynamic driving task, even if a human driver does not respondappropriately to a request to intervene. A Level Five system indicates“full automation,” referring to the full-time performance by anautomated driving system of all aspects of the dynamic driving taskunder all roadway and environmental conditions that can be managed by ahuman driver.

As shown, the autonomous vehicle 10 generally includes a propulsionsystem 20, a transmission system 22, a steering system 24, a brakesystem 26, a sensor system 28, an actuator system 30, at least one datastorage device 32, at least one controller 34, and a communicationsystem 36. The propulsion system 20 may, in various embodiments, includean internal combustion engine, an electric machine such as a tractionmotor, and/or a fuel cell propulsion system. The transmission system 22is configured to transmit power from the propulsion system 20 to thevehicle wheels 16-18 according to selectable speed ratios. According tovarious embodiments, the transmission system 22 may include a step-ratioautomatic transmission, a continuously-variable transmission, or otherappropriate transmission. The brake system 26 is configured to providebraking torque to the vehicle wheels 16-18. The brake system 26 may, invarious embodiments, include friction brakes, brake by wire, aregenerative braking system such as an electric machine, and/or otherappropriate braking systems. The steering system 24 influences aposition of the of the vehicle wheels 16-18. While depicted as includinga steering wheel for illustrative purposes, in some embodimentscontemplated within the scope of the present disclosure, the steeringsystem 24 may not include a steering wheel.

The sensor system 28 includes one or more sensing devices 40 a-40 n thateach include a field of view for sensing observable conditions of theexterior environment and/or the interior environment of the autonomousvehicle 10. The sensing devices 40 a-40 n can include, but are notlimited to, radars, lidars, optical cameras, thermal cameras, ultrasonicsensors, and/or other sensors. In addition, one or more of the sensingdevices 40 a-40 n may comprise chassis sensing devices, which observe amovement of the chassis 12, such as a pitch or a roll of the chassis 12,and generate sensor signals based thereon. Thus, one or more of thesensing devices 40 a-40 n may comprise an inertial measurement unit(IMU) that observes the pitch and the roll of the chassis 12. Inaddition, one or more of the sensing devices 40 a-40 n is movable in twodegrees of freedom. For example, with reference to FIG. 1A, a schematicillustration of the sensing device 40 a is shown. It will be understoodthat one or more of the remaining sensing devices 40 b-40 n may the sameas or substantially similar to the sensing device 40 a, and for the easeof description, a single one of the sensing devices 40 a-40 n will bediscussed herein. In this example, the sensing device 40 a has a sensor43 a with a 360 degree field of view 41 a (a portion of which is shownin FIG. 1A). The sensing device 40 a is movable about a first axis orX-axis (roll) and a second or Y-axis (pitch) based on one or morecontrol signals received from the controller 34. In this example, thesensing device 40 a includes the sensor 43 a and a sensor platform 45 a.The sensor 43 a observes the exterior environment and/or the interiorenvironment of the autonomous vehicle 10 and generates sensor signalsbased thereon. The sensor 43 a includes, but is not limited to, radars,lidars, optical cameras, thermal cameras, ultrasonic sensors, and/orother sensors.

The sensor platform 45 a is responsive to the one or more controlsignals to move the sensor 43 a such that the field of view 41 a of thesensor 43 a is aligned with an area of interest 47 a. In this example,the area of interest 47 a is a portion of a roadway associated with theplanned path of the autonomous vehicle 10. As the sensor platform 45 ais coupled to a portion of the autonomous vehicle 10, a movement of theautonomous vehicle 10 in pitch (Y-axis) and/or roll (X-axis) results ina corresponding movement of the sensing device 40 a. Thus, in certaininstances, such as during a descent down a hill or a climb up a hill,the sensor 43 a of the sensing device 40 a may not be aligned with thearea of interest 47 a. In this regard, during a descent down a hill, thesensor 43 a of the sensing device 40 a may be need to be orientatedupward (rotated about the Y-axis) to capture the area of interest 47 a,while during a climb of a hill, the sensor 43 a may need to beorientated downward (rotated about the Y-axis) to capture the area ofinterest 47 a. As a further example, during a cornering maneuver, thesensor 43 a may need to be rotated about the X-axis to capture the areaof interest 47 a. The sensor platform 45 a includes one or moreactuators 49 a, which are responsive to the one or more control signalsfrom the controller 34 to move the sensor 43 a about the Y-axis (pitch)and/or the X-axis (roll). In various embodiments, the sensor platform 45a is the sensor platform system 100 of U.S. patent application Ser. No.15/197,654, filed on Jun. 29, 2016, titled “SYSTEMS AND METHODS FORSENSOR PLATFORM” to co-inventor Yung-Chang Ko, the relevant portion ofwhich is incorporated herein by reference in its entirety. In otherembodiments, the sensor platform 45 a includes one or more gimbalplatforms having one or more actuators that are responsive to the one ormore control signals from the controller 34 to move the sensor 43 aabout the Y-axis (pitch) and/or the X-axis (roll).

With reference back to FIG. 1, the actuator system 30 includes one ormore actuator devices 42 a-42 n that control one or more vehiclefeatures such as, but not limited to, the propulsion system 20, thetransmission system 22, the steering system 24, and the brake system 26.In various embodiments, the vehicle features can further includeinterior and/or exterior vehicle features such as, but are not limitedto, doors, a trunk, and cabin features such as air, music, lighting,etc. (not numbered).

The communication system 36 is configured to wirelessly communicateinformation to and from other entities 48, such as but not limited to,other vehicles (“V2V” communication), infrastructure (“V2I”communication), remote systems, and/or personal devices (described inmore detail with regard to FIG. 2). In an exemplary embodiment, thecommunication system 36 is a wireless communication system configured tocommunicate via a wireless local area network (WLAN) using IEEE 802.11standards or by using cellular data communication. However, additionalor alternate communication methods, such as a dedicated short-rangecommunications (DSRC) channel, are also considered within the scope ofthe present disclosure. DSRC channels refer to one-way or two-wayshort-range to medium-range wireless communication channels specificallydesigned for automotive use and a corresponding set of protocols andstandards.

The data storage device 32 stores data for use in automaticallycontrolling the autonomous vehicle 10. In various embodiments, the datastorage device 32 stores defined maps of the navigable environment. Invarious embodiments, the defined maps may be predefined by and obtainedfrom a remote system (described in further detail with regard to FIG.2). For example, the defined maps may be assembled by the remote systemand communicated to the autonomous vehicle 10 (wirelessly and/or in awired manner) and stored in the data storage device 32. As can beappreciated, the data storage device 32 may be part of the controller34, separate from the controller 34, or part of the controller 34 andpart of a separate system.

The controller 34 includes at least one processor 44 and a computerreadable storage device or media 46. The processor 44 can be any custommade or commercially available processor, a central processing unit(CPU), a graphics processing unit (GPU), an auxiliary processor amongseveral processors associated with the controller 34, a semiconductorbased microprocessor (in the form of a microchip or chip set), amacroprocessor, any combination thereof, or generally any device forexecuting instructions. The computer readable storage device or media 46may include volatile and nonvolatile storage in read-only memory (ROM),random-access memory (RAM), and keep-alive memory (KAM), for example.KAM is a persistent or non-volatile memory that may be used to storevarious operating variables while the processor 44 is powered down. Thecomputer-readable storage device or media 46 may be implemented usingany of a number of known memory devices such as PROMs (programmableread-only memory), EPROMs (electrically PROM), EEPROMs (electricallyerasable PROM), flash memory, or any other electric, magnetic, optical,or combination memory devices capable of storing data, some of whichrepresent executable instructions, used by the controller 34 incontrolling the autonomous vehicle 10.

The instructions may include one or more separate programs, each ofwhich comprises an ordered listing of executable instructions forimplementing logical functions. The instructions, when executed by theprocessor 44, receive and process signals from the sensor system 28,perform logic, calculations, methods and/or algorithms for automaticallycontrolling the components of the autonomous vehicle 10, and generatecontrol signals to the actuator system 30 to automatically control thecomponents of the autonomous vehicle 10 based on the logic,calculations, methods, and/or algorithms. Although only one controller34 is shown in FIG. 1, embodiments of the autonomous vehicle 10 caninclude any number of controllers 34 that communicate over any suitablecommunication medium or a combination of communication mediums and thatcooperate to process the sensor signals, perform logic, calculations,methods, and/or algorithms, and generate control signals toautomatically control features of the autonomous vehicle 10.

In various embodiments, one or more instructions of the controller 34are embodied in the sensing device field of view control system 100 and,when executed by the processor 44, cause the processor 44 to calculate,determine or generate one or more control signals for the actuators 49a-49 n to move the sensing devices 40 a-40 n to a position thatmaintains an alignment of the field of view 41 a-41 n of the sensor 43a-43 n during pitch and/or roll of the autonomous vehicle 10. Forexample, as described in greater detail below in the context of FIGS.4-7, the instructions may cause the processor 44 to obtain, either fromthe at least one data storage device 32 or another entity 48 (e.g., anetworked map database), elevation, pitch and/or curvature data for theplanned path in front of and at the current vehicle position, and thenutilize the elevation, pitch and/or curvature data to derive theposition for the one or more of the sensing devices 40 a-40 n such thatthe field of view 41 a-41 n of the sensing devices 40 a-40 n is alignedwith the area of interest 47 a-47 n during the pitch and/or roll of theautonomous vehicle 10.

In various embodiments, the processor 44 obtains data or informationthat characterizes the field of view 41 a-41 n of one or more onboardsensing devices 40 a-40 n, and using the elevation changes and roadwaypitch at or near the current location of the vehicle 10 along with anobserved pitch of the chassis 12 of the vehicle 10, the processor 44determines a movement for one or more respective sensing devices 40 a-40n to maintain the field of view 41 a-41 n aligned with the area ofinterest 47 a-47 n based on the relationship between the field of view41 a-41 n and the pitch of the chassis 12. Thus, when the roadway pitchand elevation changes alter the alignment of the field of view 41 a-41 nwith the area of interest 47 a-47 n at a particular location, theposition of the respective one or more of the sensing devices 40 a-40 nis controlled in a corresponding manner to maintain the field of view 41a-41 n of the sensor 43 a-43 n of the sensing device 40 a-40 n alignedwith the area of interest 47 a-47 n at that location. This may, forexample, cause the one or more sensing devices 40 a-40 n to be rotatedrelative to the Y-axis at the crest or trough of a relatively steephill.

In various embodiments, the processor 44 obtains data or informationthat characterizes the field of view 41 a-41 n of one or more onboardsensing devices 40 a-40 n, and using the curvature of the roadway at ornear the current location of the vehicle 10 along with an observed rollof the chassis 12 of the vehicle 10, the processor 44 determines amovement for one or more respective sensing devices 40 a-40 n tomaintain the field of view 41 a-41 n aligned with the area of interest47 a-47 n based on the relationship between the field of view 41 a-41 nand the roll of the chassis 12. Thus, when the roadway curvature changesalter the alignment of the field of view 41 a-41 n with the area ofinterest 47 a-47 n at a particular location, the position of therespective one or more of the sensing devices 40 a-40 n is controlled ina corresponding manner to maintain the field of view 41 a-41 n of thesensor 43 a-43 n of the sensing device 40 a-40 n aligned with the areaof interest 47 a-47 n at that location. This may, for example, cause theone or more sensing devices 40 a-40 n to be rotated relative to theX-axis during a cornering maneuver of the autonomous vehicle 10.

With reference now to FIG. 2, in various embodiments, the autonomousvehicle 10 described with regard to FIG. 1 may be suitable for use inthe context of a taxi or shuttle system in a certain geographical area(e.g., a city, a school or business campus, a shopping center, anamusement park, an event center, or the like) or may simply be managedby a remote system. For example, the autonomous vehicle 10 may beassociated with an autonomous vehicle based remote transportationsystem. FIG. 2 illustrates an exemplary embodiment of an operatingenvironment shown generally at 50 that includes an autonomous vehiclebased remote transportation system 52 that is associated with one ormore autonomous vehicles 10 a-10 n as described with regard to FIG. 1.In various embodiments, the operating environment 50 further includesone or more user devices 54 that communicate with the autonomous vehicle10 and/or the remote transportation system 52 via a communicationnetwork 56.

The communication network 56 supports communication as needed betweendevices, systems, and components supported by the operating environment50 (e.g., via tangible communication links and/or wireless communicationlinks). For example, the communication network 56 can include a wirelesscarrier system 60 such as a cellular telephone system that includes aplurality of cell towers (not shown), one or more mobile switchingcenters (MSCs) (not shown), as well as any other networking componentsrequired to connect the wireless carrier system 60 with a landcommunications system. Each cell tower includes sending and receivingantennas and a base station, with the base stations from different celltowers being connected to the MSC either directly or via intermediaryequipment such as a base station controller. The wireless carrier system60 can implement any suitable communications technology, including forexample, digital technologies such as CDMA (e.g., CDMA2000), LTE (e.g.,4G LTE or 5G LTE), GSM/GPRS, or other current or emerging wirelesstechnologies. Other cell tower/base station/MSC arrangements arepossible and could be used with the wireless carrier system 60. Forexample, the base station and cell tower could be co-located at the samesite or they could be remotely located from one another, each basestation could be responsible for a single cell tower or a single basestation could service various cell towers, or various base stationscould be coupled to a single MSC, to name but a few of the possiblearrangements.

Apart from including the wireless carrier system 60, a second wirelesscarrier system in the form of a satellite communication system 64 can beincluded to provide uni-directional or bi-directional communication withthe autonomous vehicles 10 a-10 n. This can be done using one or morecommunication satellites (not shown) and an uplink transmitting station(not shown). Uni-directional communication can include, for example,satellite radio services, wherein programming content (news, music,etc.) is received by the transmitting station, packaged for upload, andthen sent to the satellite, which broadcasts the programming tosubscribers. Bi-directional communication can include, for example,satellite telephony services using the satellite to relay telephonecommunications between the vehicle 10 and the station. The satellitetelephony can be utilized either in addition to or in lieu of thewireless carrier system 60.

A land communication system 62 may further be included that is aconventional land-based telecommunications network connected to one ormore landline telephones and connects the wireless carrier system 60 tothe remote transportation system 52. For example, the land communicationsystem 62 may include a public switched telephone network (PSTN) such asthat used to provide hardwired telephony, packet-switched datacommunications, and the Internet infrastructure. One or more segments ofthe land communication system 62 can be implemented through the use of astandard wired network, a fiber or other optical network, a cablenetwork, power lines, other wireless networks such as wireless localarea networks (WLANs), or networks providing broadband wireless access(BWA), or any combination thereof. Furthermore, the remotetransportation system 52 need not be connected via the landcommunication system 62, but can include wireless telephony equipment sothat it can communicate directly with a wireless network, such as thewireless carrier system 60.

Although only one user device 54 is shown in FIG. 2, embodiments of theoperating environment 50 can support any number of user devices 54,including multiple user devices 54 owned, operated, or otherwise used byone person. Each user device 54 supported by the operating environment50 may be implemented using any suitable hardware platform. In thisregard, the user device 54 can be realized in any common form factorincluding, but not limited to: a desktop computer; a mobile computer(e.g., a tablet computer, a laptop computer, or a netbook computer); asmartphone; a video game device; a digital media player; a piece of homeentertainment equipment; a digital camera or video camera; a wearablecomputing device (e.g., smart watch, smart glasses, smart clothing); orthe like. Each user device 54 supported by the operating environment 50is realized as a computer-implemented or computer-based device havingthe hardware, software, firmware, and/or processing logic needed tocarry out the various techniques and methodologies described herein. Forexample, the user device 54 includes a microprocessor in the form of aprogrammable device that includes one or more instructions stored in aninternal memory structure and applied to receive binary input to createbinary output. In some embodiments, the user device 54 includes a GPSmodule capable of receiving GPS satellite signals and generating GPScoordinates based on those signals. In other embodiments, the userdevice 54 includes cellular communications functionality such that thedevice carries out voice and/or data communications over thecommunication network 56 using one or more cellular communicationsprotocols, as are discussed herein. In various embodiments, the userdevice 54 includes a visual display, such as a touch-screen graphicaldisplay, or other display.

The remote transportation system 52 includes one or more backend serversystems, which may be cloud-based, network-based, or resident at theparticular campus or geographical location serviced by the remotetransportation system 52. The remote transportation system 52 can bemanned by a live advisor, or an automated advisor, or a combination ofboth. The remote transportation system 52 can communicate with the userdevices 54 and the autonomous vehicles 10 a-10 n to schedule rides,dispatch autonomous vehicles 10 a-10 n, and the like. In variousembodiments, the remote transportation system 52 stores accountinformation such as subscriber authentication information, vehicleidentifiers, profile records, behavioral patterns, and other pertinentsubscriber information.

In accordance with a typical use case workflow, a registered user of theremote transportation system 52 can create a ride request via the userdevice 54. The ride request will typically indicate the passenger'sdesired pickup location (or current GPS location), the desireddestination location (which may identify a predefined vehicle stopand/or a user-specified passenger destination), and a pickup time. Theremote transportation system 52 receives the ride request, processes therequest, and dispatches a selected one of the autonomous vehicles 10a-10 n (when and if one is available) to pick up the passenger at thedesignated pickup location and at the appropriate time. The remotetransportation system 52 can also generate and send a suitablyconfigured confirmation message or notification to the user device 54,to let the passenger know that a vehicle is on the way.

As can be appreciated, the subject matter disclosed herein providescertain enhanced features and functionality to what may be considered asa standard or baseline autonomous vehicle 10 and/or an autonomousvehicle based remote transportation system 52. To this end, anautonomous vehicle and autonomous vehicle based remote transportationsystem can be modified, enhanced, or otherwise supplemented to providethe additional features described in more detail below.

In accordance with various embodiments, controller 34 implements anautonomous driving system (ADS) 70 as shown in FIG. 3. That is, suitablesoftware and/or hardware components of controller 34 (e.g., processor 44and computer-readable storage device 46) are utilized to provide anautonomous driving system 70 that is used in conjunction with vehicle10.

In various embodiments, the instructions of the autonomous drivingsystem 70 may be organized by function or system. For example, as shownin FIG. 3, the autonomous driving system 70 can include a sensor fusionsystem 74, a positioning system 76, a guidance system 78, and a vehiclecontrol system 80. As can be appreciated, in various embodiments, theinstructions may be organized into any number of systems (e.g.,combined, further partitioned, etc.) as the disclosure is not limited tothe present examples.

In various embodiments, the sensor fusion system 74 synthesizes andprocesses sensor data and predicts the presence, location,classification, and/or path of objects and features of the environmentof the vehicle 10. In various embodiments, the sensor fusion system 74can incorporate information from multiple sensors, including but notlimited to cameras, lidars, radars, and/or any number of other types ofsensors.

The positioning system 76 processes sensor data along with other data todetermine a position (e.g., a local position relative to a map, an exactposition relative to lane of a road, vehicle heading, velocity, etc.) ofthe vehicle 10 relative to the environment. The guidance system 78processes sensor data along with other data to determine a path for thevehicle 10 to follow. The vehicle control system 80 generates controlsignals for controlling the vehicle 10 according to the determined path.

In various embodiments, the controller 34 implements machine learningtechniques to assist the functionality of the controller 34, such asfeature detection/classification, obstruction mitigation, routetraversal, mapping, sensor integration, ground-truth determination, andthe like.

As mentioned briefly above, the sensing device field of view controlsystem 100 of FIG. 1 is included within the autonomous driving system70, and for example, is embedded within or associated with the guidancesystem 78. In this example, the sensing device field of view controlsystem 100 receives the position of the vehicle 10 from the positioningsystem 76, sensor signals from the sensor system 28 and receives thedetermined path from the guidance system 78. Based on the position ofthe vehicle 10, the sensor signals from the sensor system 28 and thedetermined path for the vehicle 10, the sensing device field of viewcontrol system 100 determines the position for the sensors 43 a-43 n ofthe sensing devices 40 a-40 n to maintain the field of view 41 a-41 naligned with the area of interest 47 a-47 n as the vehicle 10 movesalong the determined path. In this regard, the sensing device field ofview control system 100 outputs one or more control signals to theactuators 49 a-49 n of the sensing devices 40 a-40 n to move the sensors43 a-43 n of the sensing devices 40 a-40 n to a position that maintainsthe alignment of the field of view 41 a-41 n with the area of interest47 a-47 n. Generally, the sensing device field of view control system100 outputs one or more control signals to the actuators 49 a-49 n ofthe sensing devices 40 a-40 n based on inputs received from othermodules of the vehicle 10 and based on the control systems and methodsof the present disclosure.

For example, as shown in more detail with regard to FIG. 4 and withcontinued reference to FIG. 3, a dataflow diagram illustrates variousembodiments of the sensing device field of view control system 100 forthe sensing devices 40 a-40 n, which may be embedded within thecontroller 34. Various embodiments of the sensing device field of viewcontrol system 100 according to the present disclosure can include anynumber of sub-modules embedded within the controller 34. As can beappreciated, the sub-modules shown in FIG. 4 can be combined and/orfurther partitioned to similarly control the one or more actuators 49a-49 n. Inputs to the sensing device field of view control system 100may be received from the positioning system 76 (FIG. 3), received fromthe guidance system 78 (FIG. 3), received from the sensor system 28(FIG. 1), received from other control modules (not shown) associatedwith the vehicle 10, and/or determined/modeled by other sub-modules (notshown) within the controller 34. In various embodiments, the sensingdevice field of view control system 100 includes a map datastore 300, apitch compensation module 302, a pitch datastore 313, a rollcompensation module 304, a roll datastore 319, a sensor datastore 306,an interest data datastore 307 and a sensor control module 308.

The map datastore 300 stores map information 310 of known roadway pitchand/or inclines and map information of known roadway curvature 312. Themap information 310 includes maps of various areas and indications oflocations within the map of known roadway pitch and/or inclines. Theroadway pitch and/or incline is predefined (e.g., defined by a user ofthe remote transportation system 52 and communicated to the vehicle 10)and/or learned over time (e.g., learned based on repeated input from theone or more sensing devices 40 a-40 n, or other method of learning) bythe vehicle 10 or other vehicles 10 a-10 n. The map information 312includes maps of various areas and indications of locations within themap of known roadway curvatures. The roadway curvature is predefined(e.g., defined by a user of the remote transportation system 52 andcommunicated to the vehicle 10) and/or learned over time (e.g., learnedbased on repeated input from the one or more sensing devices 40 a-40 n,or other method of learning) by the vehicle 10 or other vehicles 10 a-10n.

The pitch datastore 313 stores data of the determined planned pitch ofthe vehicle 10 over a longitudinal prediction horizon associated with ageographical coordinate location of the vehicle 10. The pitch datastore313 is populated by the pitch compensation module 302 during the travelof the vehicle 10 along the planned path.

The pitch compensation module 302 receives as input position data 314.The position data 314 is the current position of the vehicle 10, whichincludes a local position relative to a map, an exact position relativeto lane of a road, vehicle heading, velocity, etc. In variousembodiments, the position data 314 is received from the positioningsystem 76 and is provided in a geographical or world coordinate system.The pitch compensation module 302 also receives as input path data 316.The path data 316 is the planned or determined path for the vehicle 10,as determined by the guidance system 78, which may include geographicalcoordinate locations for the vehicle 10 to arrive at a destination. Thepath data 316 may include, for example, information about the route thevehicle 10 is traveling along to reach a destination from the currentposition, including current and future lane information (e.g., lanetypes, boundaries, and other constraints or restrictions).

Based on the current position of the vehicle 10 and the determined path,the pitch compensation module 302 retrieves the map information 310 fromthe map datastore 300. The pitch compensation module 302 may alsoretrieve the map information 310 that encompasses some additionalforward travel distance into the future near the current position of thevehicle 10 (based on the determined path), which may be considered alongitudinal prediction horizon. The map information 310 may includesurveyed elevation or pitch data along the roadway on which the vehicle10 is traveling at locations corresponding to the geographicalcoordinate locations defined in the path data 316. In other embodiments,the map information 310 includes lower resolution surveyed elevation orpitch data along the roadway on which the vehicle 10 is traveling atrelatively fewer locations, which, in turn is interpolated orextrapolated to obtain elevation and pitch data for the current positionof the vehicle 10 and/or at geographical coordinate locations associatedwith the path data 316. In other yet embodiments, the map information310 may include surveyed elevation data for a geographic region that isindependent of roadways, which, in turn, is interpolated or extrapolatedto obtain estimated elevation data for the current position of thevehicle 10 and/or at geographical coordinate locations associated withthe path data 316 from which roadway pitch can be calculated orotherwise determined.

In addition, it should be noted that while the subject matter may bedescribed herein primarily in the context of using a map database,mapping data, survey data, or the like to obtain roadway pitch datacorresponding to the current position of the vehicle 10 and/or futurelocations along the planned route of a vehicle, the subject matterdescribed herein is not necessarily limited to mapped pitch data. Forexample, in various embodiments, future roadway pitch data may beobtained from or otherwise determined in realtime using one or moresensing devices 40 a-40 n onboard the vehicle 10, such as, for example,cameras or other imaging sensors, radar, lidar, or other rangingdevices, inertial measurement units (IMUs) or other inertial sensorsonboard the vehicle 10, and the like. Additionally, in variousembodiments, future roadway pitch data may be calculated or determinedby blending, fusing, or otherwise augmenting pitch data derived frommapping data with sensed pitch data determined using onboard sensingdevices 40 a-40 n. Accordingly, the subject matter described herein isnot intended to be limited to any particular source of roadway pitchdata for future locations in advance of a current vehicle location alonga planned route of travel.

As described above, in one or more embodiments, the pitch compensationmodule 302 may also retrieve or otherwise obtain roadway pitch data fora portion of a route within the longitudinal prediction horizon usingthe one or more onboard sensing devices 40 a-40 n. In this regard, ifthe resolution of the map database does not provide sufficient pitchdata for various locations within the longitudinal prediction horizon,one or more of the onboard sensing devices 40 a-40 n may be utilized tocalculate or otherwise determine pitch data for those locations withinthe longitudinal prediction horizon. For example, pitch data derivedusing onboard sensing devices 40 a-40 n may be utilized to fill gaps inthe pitch data derived from the map database (e.g., by substitutingsensor-derived pitch data at locations lacking map-based pitch data), orpitch data derived using onboard sensors for various locations ahead ofthe vehicle 10 along the route may be fused or blended with pitch dataderived from the map database (e.g., by interpolating pitch data pointsto fill gaps within the longitudinal prediction horizon).

In one embodiment, the pitch compensation module 302 may analyze therelative qualities of the pitch data available from the differentsources and select which source of pitch data to be utilized orotherwise determine the manner in which the pitch data from differentsources is to be utilized. For example, in one embodiment, the pitchcompensation module 302 analyzes the map-based pitch data within thelongitudinal prediction horizon to determine whether the pitch datasatisfies one or more quality criteria. The quality criteria, caninclude, for example, a resolution or accuracy criterion (e.g., athreshold level of resolution, or other criteria), a temporal criterion(e.g., map-based pitch data was obtained within some threshold timeperiod before the current time), a reliability criterion (e.g.,reliability metric associated with the source, or other criteria) and/orthe like to ensure that the map-based pitch data is sufficientlyreliable.

When the map-based pitch data fails to satisfy one or more qualitycriteria, the pitch compensation module 302 analyzes the pitch dataderived from the onboard sensing devices 40 a-40 n or other onboardsources to identify which source should be utilized in lieu of or inaddition to the map-based pitch data. For example, the pitchcompensation module 302 may identify or otherwise select thesensor-based pitch data having values that meet the quality criteria andutilize the selected sensor-based pitch data in determiningpitch-compensated constraints. In various embodiments, the sensor-basedpitch data may be fused, blended, or otherwise combined with themap-based pitch data to achieve the required quality. For example, themap-based data may be used to fill gaps or blind spots in thesensor-based pitch data or vice versa.

In various embodiments, the pitch compensation module 302 processes themap information 310 (and/or data from other sources) and determines aplanned pitch 311 of the vehicle 10 at the current position of thevehicle 10 defined in the position data 314 and a planned pitch for thevehicle 10 for a longitudinal prediction horizon based on the path data316. In one example, based on the map information 310, the pitchcompensation module 302 determines what position the chassis 12 isorientated at the current position of the vehicle 10 and for thelongitudinal prediction horizon. In this example, the pitch compensationmodule 302 determines the planned pitch 311 for the current geographicalcoordinate location of the vehicle 10 in a geographical or worldcoordinate system and determines the planned pitch 311 for thelongitudinal prediction horizon in a geographical or world coordinatesystem. For the current geographical coordinate location of the vehicle10, the pitch compensation module 302 performs a coordinatetransformation to translate the planned pitch 311 into the vehiclecoordinate system and for the longitudinal prediction horizon, the pitchcompensation module 302 performs a coordinate transformation totranslate the planned pitch 311 into the vehicle coordinate system. Thepitch compensation module 302 stores the planned pitch 311 for thecurrent position of the vehicle 10 and the planned pitch 311 for thelongitudinal prediction horizon associated with the respectivegeographical coordinate locations of the vehicle 10 in the longitudinalprediction horizon in the pitch datastore 313.

The pitch compensation module 302 also receives as input chassis pitchdata 320. The chassis pitch data 320 is a substantially real-time pitchof the vehicle 10 at the current position of the vehicle 10, as observedby one or more sensing devices 40 a-40 n associated with the chassis 12of the vehicle 10. For example, the chassis 12 of the vehicle 10 maypitch in an unplanned manner due to encountered characteristics of theroadway, such as bumps, potholes, etc. Generally, as the one or moresensing devices 40 a-40 n are coupled to the vehicle 10, the observedsubstantially real-time pitch of the vehicle 10 is provided in thevehicle coordinate system, which may take the force of gravity intoconsideration to determine the position of the chassis 12 (via anattitude estimation technique involving one of the sensing devices 40a-40 n, such as an inertial measurement unit (IMU)). It should be notedthat the chassis pitch data 320 may also be received from other modulesassociated with the vehicle 10.

Based on the chassis pitch data 320 and the position data 314, the pitchcompensation module 302 queries the pitch datastore 313 and retrievesthe planned pitch 311 for the current position (current geographicallocation) of the vehicle 10. Based on the chassis pitch data 320 and theplanned pitch 311, the pitch compensation module 302 determines a pitch318 of the vehicle 10. In one example, the pitch compensation module 302modifies the planned pitch 311 retrieved from the pitch datastore 313(determined from the map information 310 and/or other sources) with thesubstantially real-time pitch from the chassis pitch data 320 todetermine the pitch 318. In one example, the pitch compensation module302 subtracts the chassis pitch data 320 from the planned pitch 311 todetermine the pitch 318. Thus, the pitch 318 is the planned andunplanned (e.g., observed by the sensing devices 40 a-40 n) pitch of thechassis 12 of the vehicle 10 at the current position of the vehicle 10.The pitch compensation module 302 sets the pitch 318 for the sensorcontrol module 308. In one example, the pitch compensation module 302may set the pitch 318 as a positive or negative rotation about theY-axis.

The roll datastore 319 stores data of the determined planned roll of thevehicle 10 over a lateral prediction horizon associated with ageographical coordinate location of the vehicle 10. The roll datastore319 is populated by the roll compensation module 304 during the travelof the vehicle 10 along the planned path.

The roll compensation module 304 receives as input the position data 314and the path data 316. Based on the current position of the vehicle 10and the determined path, the roll compensation module 304 retrieves themap information 312 from the map datastore 300. The roll compensationmodule 304 may also retrieve the map information 312 that encompassessome additional forward travel distance into the future near the currentposition of the vehicle 10 (based on the determined path), which may beconsidered a lateral prediction horizon. The map information 312 mayinclude surveyed curvature data along the roadway on which the vehicleis traveling at locations corresponding to the current position of thevehicle 10 and/or at geographical coordinate locations defined in thepath data 316. In other embodiments, the map information 312 includeslower resolution surveyed curvature data along the roadway on which thevehicle is traveling at relatively fewer locations, which, in turn isinterpolated or extrapolated to obtain curvature data for the currentposition of the vehicle 10 and/or at geographical coordinate locationsassociated with the path data 316.

In addition, it should be noted that while the subject matter may bedescribed herein primarily in the context of using a map database,mapping data, survey data, or the like to obtain roadway roll datacorresponding to the current position of the vehicle 10 and/or futurelocations along the planned route of a vehicle, the subject matterdescribed herein is not necessarily limited to mapped roll data. Forexample, in various embodiments, future roadway roll data may beobtained from or otherwise determined using one or more sensing devices40 a-40 n onboard the vehicle, such as, for example, cameras or otherimaging sensors, radar, lidar, or other ranging devices, inertialmeasurement units (IMUs) or other inertial sensors onboard the vehicle,and the like. Additionally, in various embodiments, future roadway rolldata may be calculated or determined by blending, fusing, or otherwiseaugmenting roll data derived from mapping data with sensed roll datadetermined using onboard sensing devices 40 a-40 n. Accordingly, thesubject matter described herein is not intended to be limited to anyparticular source of roadway roll data for future locations in advanceof a current vehicle location along a planned route of travel.

As described above, in one or more embodiments, the roll compensationmodule 304 may also retrieve or otherwise obtain roadway roll data for aportion of a route within the lateral prediction horizon using the oneor more onboard sensing devices 40 a-40 n. In this regard, if theresolution of the map database does not provide sufficient roll data forvarious locations within the lateral prediction horizon, the one or moreonboard sensing devices 40 a-40 n may be utilized to calculate orotherwise determine roll data for those locations within the lateralprediction horizon. For example, roll data derived using the onboardsensing devices 40 a-40 n may be utilized to fill gaps in the roll dataderived from the map database (e.g., by substituting sensor-derived rolldata at locations lacking map-based roll data), or roll data derivedusing the onboard sensing devices 40 a-40 n for various locations aheadof the vehicle along the route may be fused or blended with roll dataderived from the map database (e.g., by interpolating roll data pointsto fill gaps within the lateral prediction horizon).

In one embodiment, the roll compensation module 304 may analyze therelative qualities of the roll data available from the different sourcesand select which source of the roll data to be utilized or otherwisedetermine the manner in which the roll data from different sources is tobe utilized. For example, in one embodiment, the roll compensationmodule 304 analyzes the map-based roll data within the lateralprediction horizon to determine whether the roll data satisfies one ormore quality criteria. The quality criteria, can include, for example, aresolution or accuracy criterion (e.g., a threshold level of resolution,or other criteria), a temporal criterion (e.g., map-based roll data wasobtained within some threshold time period before the current time), areliability criterion (e.g., reliability metric associated with thesource, or other criteria) and/or the like to ensure that the map-basedroll data is sufficiently reliable.

When the map-based roll data fails to satisfy one or more qualitycriteria, the roll compensation module 304 analyzes the roll dataderived from the onboard sensing devices 40 a-40 n or other onboardsources to identify which source should be utilized in lieu of or inaddition to the map-based roll data. For example, the roll compensationmodule 304 may identify or otherwise select the sensor-based roll datahaving values that meet the quality criteria and utilize the selectedsensor-based roll data in determining roll-compensated constraints. Invarious embodiments, the sensor-based roll data may be fused, blended,or otherwise combined with the map-based roll data to achieve therequired quality. For example, the map-based data may be used to fillgaps or blind spots in the sensor-based roll data or vice versa.

In various embodiments, the roll compensation module 304 processes themap information 312 (or data from other sources) and determines aplanned roll 317 of the vehicle 10 at the current position of thevehicle 10 defined in the position data 314 and for a lateral predictionhorizon based on the path data 316. In one example, based on the mapinformation 312 (and/or other sources), the roll compensation module 304determines what position the chassis 12 is orientated at the currentposition of the vehicle 10 and determines what position the chassis 12is orientated at over the lateral prediction horizon. In this example,the roll compensation module 304 determines the planned roll 317 for thecurrent geographical location of the vehicle 10 in a geographical orworld coordinate system and determines the planned roll 317 for thelateral prediction horizon in a geographical or world coordinate system.For the current geographical coordinate location of the vehicle 10, theroll compensation module 304 performs a coordinate transformation totranslate the planned roll 317 into the vehicle coordinate system andfor the lateral prediction horizon, the roll compensation module 304performs a coordinate transformation to translate the planned roll 317into the vehicle coordinate system. The roll compensation module 304stores the planned roll 317 for the current position of the vehicle 10and the planned roll 317 for the lateral prediction horizon associatedwith the respective geographical coordinate locations of the vehicle 10in the lateral prediction horizon in the roll datastore 319.

The roll compensation module 304 also receives as input chassis rolldata 324. The chassis roll data 324 is a substantially real-time roll ofthe vehicle 10 at the current position of the vehicle 10, as observed byone or more sensing devices 40 a-40 n associated with the chassis 12 ofthe vehicle 10. For example, the chassis 12 of the vehicle 10 may rollin an unplanned manner due to encountered obstacles, etc. Generally, asthe one or more sensing devices 40 a-40 n are coupled to the vehicle 10,the observed substantially real-time roll of the vehicle 10 is providedin the vehicle coordinate system, which may take the force of gravityinto consideration to determine the position of the chassis 12 (via anattitude estimation technique involving one of the sensing devices 40a-40 n, such as an inertial measurement unit (IMU)). It should be notedthat the chassis roll data 324 may also be received from other modulesassociated with the vehicle 10.

Based on the chassis roll data 324 and the position data 314, the rollcompensation module 304 queries the roll datastore 319 and retrieves theplanned roll 317 for the current position of the vehicle 10. Based onthe chassis roll data 324 and the planned roll 317, the rollcompensation module 304 determines a roll 322 of the vehicle 10. In oneexample, the roll compensation module 304 modifies the planned roll 317retrieved from the roll datastore 319 (determined from the mapinformation 312) with the substantially real-time roll to determine theroll 322. In one example, the roll compensation module 304 subtracts thechassis roll data 324 from the planned roll 317 to determine the roll322. Thus, the roll 322 is the roll of the chassis 12 of the vehicle 10at the current position of the vehicle 10. The roll compensation module304 sets the roll 322 for the sensor control module 308. In one example,the roll compensation module 304 may set the roll 322 as a positive ornegative rotation about the X-axis.

The sensor datastore 306 stores data indicating sensor characteristics328 of the sensing devices 40 a-40 n. For each sensing device 40 a-40 n,the sensor characteristics 328 includes at least the vehicle coordinatelocations of the field of view 41 a-41 n of each of the sensors 43 a-43n associated with the respective sensing device 40 a-40 n. The sensorcharacteristics 328 may also include other constraints associated witheach of the sensing devices 40 a-40 n, such as a beamwidth, limits to anamount of movement of each of the sensor platforms 45 a-45 n, movementrate limits for the actuators 49 a-49 n, slew rates for the actuators 49a-49 n, etc. The sensor characteristics 328 in the sensor datastore 306are predefined and may be factory-set. Alternatively, the sensorcharacteristics 328 may be predefined by a user of remote transportationsystem 52 and communicated to the vehicle 10.

The interest data datastore 307 stores pre-defined data indicating areasof interest 330 along the planned path of the vehicle 10. The areas ofinterest 330 include geographical or world coordinate locations for apre-defined area of interest 47 a-47 n during the travel of the vehicle10 along the planned path (e.g., a geographical coordinate location ofan intersection, etc.). In various embodiments, the areas of interest330 stored in the interest data datastore 307 are pre-defined (e.g.,defined by a user of the remote transportation system 52 andcommunicated to the vehicle 10) and/or learned over time (e.g., learnedbased on repeated input from the one or more sensing devices 40 a-40 n,or other method of learning) by the vehicle 10 or other vehicles 10 a-10n. Generally, each area of interest 330 comprises a geographicalcoordinate location for an alignment of each of the sensing devices 40a-40 n during the travel of the vehicle 10 along the planned path toobserve the respective area of interest 330. It should be understood,however, that the areas of interest 330 during the travel of the vehicle10 may also be received from other modules of the vehicle 10 and/or froma user of the remote transportation system 52.

The sensor control module 308 receives as input the pitch 318 and theroll 322 determined for the current position of the vehicle 10. Thesensor control module 308 also receives as input the position data 314,and based on the position data 314, retrieves the areas of interest 330for the sensor platforms 45 a-45 n based on the current position of thevehicle 10. The sensor control module 308 retrieves the sensorcharacteristics 328 for each sensing device 40 a-40 n. The sensorcontrol module 308 determines, for each sensing device 40 a-40 n,whether the field of view 41 a-41 n of the sensing device 40 a-40 n isaligned with the area of interest 47 a-47 n at the current position ofthe vehicle 10 based on the pitch 318 and the roll 322.

In this regard, in one example, the sensor control module 308 determinesthe vehicle coordinate location for the field of view 41 a-41 n of eachsensing device 40 a-40 n based on the pitch 318 and the roll 322. Sincethe sensing devices 40 a-40 n are each rigidly coupled to the vehicle10, as the chassis 12 is pitching and/or rolling, the sensing devices 40a-40 n are also pitching and/or rolling. The sensor control module 308updates or calculates the vehicle coordinate location for the field ofview 41 a-41 n based on the pitch 318 and/or roll 322 by adding theY-axis rotation value and/or the X-axis rotation value to the knownvehicle coordinate locations of the field of view 41 a-41 n of thesensing devices 40 a-40 n.

With the areas of interest 330 retrieved from the interest datadatastore 307, geometric calculations are employed that set the updatedfield of view 41 a-41 n of the respective sensing device 40 a-40 n equalto the respective geographical coordinate location of each of the areasof interest 330.

In the example of the area of interest 330 as a portion of a roadway,geometric calculations are performed to project the updated field ofview 41 a-41 n of the respective sensing device 40 a-40 n onto theroadway. For example, if the sensing device 40 a-40 n has a pre-definedlimited vertical beamwidth based on the sensor characteristics 328 andis mounted at some pre-defined height off the ground (on the vehicle 10and based on the known location of the sensing devices 40 a-40 n), thesensor control module 308 calculates the geometric equation for the line(in a 2D vertical-forward plane), or more accurately a plane (in 3Dspace), of a top of the beam (from the pre-defined vertical beamwidthand known vehicle coordinate locations of the field of view 41 a-41 n)of the respective sensing device 40 a-40 n as a function of the pitch318 and the roll 322. Stated another way, the sensor control module 308updates the known coordinate locations for a top of the field of view 41a-41 n based on the pitch 318 and the roll 322. The sensor controlmodule 308 also calculates the geometric equation for the line (in a 2Dvertical-forward plane), or more accurately a plane (in 3D space), of abottom of the beam (from the pre-defined vertical beamwidth and knownvehicle coordinate locations of the field of view 41 a-41 n) based onthe pitch 318 and the roll 322. Stated another way, the sensor controlmodule 308 updates the known coordinate locations for a bottom of thefield of view 41 a-41 n based on the pitch 318 and the roll 322.

The intersections of these lines or planes with the roadway are solvedfor geometrically such that the field of view 41 a-41 n may be expressedmathematically as a function of an actuation angle or actuation anglesof the respective sensing device 40 a-40 n. The area of interest 330covered by the field of view 41 a-41 n of the sensing device 40 a-40 nis the area along the roadway that is in between the bottom of the beamintersection with the roadway, and the top of the beam intersection withthe roadway. In other examples, the area of interest 330 retrieved fromthe interest data datastore 307 may also include height information (forexample, to aim the sensing device 40 a-40 n at a vehicle bumper'sapproximate height at some position ahead on the roadway). In thisexample, the sensor control module 308 would employ the above-describedprojection method using a vertical offset above the roadway as opposedto the roadway itself.

In certain embodiments, the field of view 41 a-41 n may not besufficient to fully cover the areas of interest 330, so an optimizationprocedure may be employed to maximize the coverage of the areas ofinterest 330 based on the geometric constraints of the sensing device 40a-40 n retrieved in the sensor characteristics 328 for each sensingdevice 40 a-40 n. In the example of the roadway as the area of interest330, the optimization procedure includes projecting the updated field ofview 41 a-41 n of the respective sensing device 40 a-40 n onto theroadway (i.e. projecting the top of the beam of the respective sensingdevice 40 a-40 n and the bottom of the beam of the respective sensingdevice 40 a-40 n onto the roadway as a function of the pitch 318 and theroll 322). The intersections of these lines or planes with the roadwayare solved for geometrically such that the field of view 41 a-41 n maybe expressed mathematically as a function of an actuation angle oractuation angles of the respective sensing device 40 a-40 n. Theoptimization procedure performed by the sensor control module 308 solvesfor the desired actuation angle or actuation angles. Depending on thegeometry of the roadway, the placement of the sensing device 40 a-40 non the vehicle 10, the actuation limits of the sensing device 40 a-40 n,and the movable field of view 41 a-41 n associated with the respectivesensing device 40 a-40 n (e.g., the vertical beamwidth of the respectivesensing device 40 a-40 n), the area of interest 330 may not be able tobe covered exactly, in which case the sensor control module 308 performsa numerical optimization procedure to attempt to maximize the area ofinterest 330 that is able to be covered. If all of the area of interest330 may be covered, the optimization procedure may minimize the movementof the respective sensing device 40 a-40 n required to cover the area ofinterest 330, may attempt to orient the desired field of view 41 a-41 nof the sensing device 40 a-40 n as much towards the center of the fieldof view 41 a-41 n of the sensing device 40 a-40 n as possible, etc.

In other embodiments, dynamic limitations of the actuator 49 a-49 n(e.g. how fast the respective actuator 49 a-49 n may move the sensor 43a-43 n based on the slew rate or rate limit associated with therespective actuator 49 a-49 n) retrieved in the sensor characteristics328 may limit the ability of the field of view 41 a-41 n of therespective sensing device 40 a-40 n to track the respective area ofinterest 330. In this example, model predictive control may be used tomaximize via mathematical optimization the predicted trajectory of thefield of view 41 a-41 n of the sensing device 40 a-40 n relative to thearea of interest 330 over time, based on the planned trajectory of thevehicle 10, planned movement of the vehicle 10 along the roadway,planned area of interest location, and known constraints of theactuators 49 a-49 n of the sensing devices 40 a-40 n. For example, ifthe sensing device 40 a-40 n has a relatively slow movement rate limitcompared to the speed at which the attitude of the vehicle 10 and theroad elevation ahead may change, the optimization procedure performed bythe sensor control module 308 may also consider the instantaneousgeometry of the situation along with these temporal constraints andinformation. Additionally, the model predictive control may take intoaccount a model of the vehicle's chassis dynamics and the planned pathin order to predict the roll and pitch of the vehicle 10 as it drivesalong the planned path to essentially predict the instantaneous roll andpitch of the vehicle 10 to construct a prediction of the adjusted rolland pitch.

The sensor control module 308 solves the geometric calculations that setthe updated field of view 41 a-41 n of the respective sensing device 40a-40 n equal to the respective geographical coordinate location of eachof the areas of interest 330 to determine an amount of movement(actuation angle(s)) about the X-axis and the Y-axis to align the fieldof view 41 a-41 n of the sensor 43 a-43 n with the area of interest 330and generates one or more control signals 332 to the respectiveactuators 49 a-49 n based on the solution. The sensor control module 308may perform the above-described optimization procedure to determine theamount of movement about the X-axis and/or the Y-axis to align the fieldof view 41 a-41 n with the area of interest 330 and generate the one ormore control signals 332 to the respective actuators 49 a-49 n based onthe optimized solution. The one or more control signals 332 command therespective actuators 49 a-49 n to move the respective sensor platforms45 a-45 n of the sensing devices 40 a-40 n about the X-axis and/orY-axis such that the field of view 41 a-41 n of the sensor 43 a-43 nmaintains alignment with the area of interest 330 at the currentlocation of the vehicle 10.

Stated another way, generally, in various embodiments, the sensorcontrol module 308 updates the known field of view 41 a-41 n of each ofthe sensing devices 40 a-40 n based on the pitch 318 and the roll 322.The sensor control module 308 sets the updated field of view 41 a-41 nequal to the known area of interest 330, as retrieved from the interestdata datastore 307, and solves to determine the actuation angle(s) oramount of movement about the X-axis and the Y-axis to align the field ofview 41 a-41 n with the area of interest 330. The sensor control module308 generates the one or more control signals 332 based on thissolution. In various embodiments, the sensor control module 308 may useone or more optimization procedures, which takes into account one ormore sensor characteristics, one or more dynamic characteristics and thelike as discussed above, to determine an optimized solution and thesensor control module 308 may generate the one or more control signals332 based on the optimized solution.

Referring now to FIG. 5, and with continued reference to FIGS. 1-4, aflowchart illustrates a control method 400 that can be performed by thesensing device field of view control system 100 of FIGS. 1-4 inaccordance with the present disclosure. In one example, the controlmethod 400 is performed by the processor 44 of the controller 34. As canbe appreciated in light of the disclosure, the order of operation withinthe method is not limited to the sequential execution as illustrated inFIG. 5, but may be performed in one or more varying orders as applicableand in accordance with the present disclosure. In various embodiments,the control method 400 can be scheduled to run based on one or morepredetermined events, and/or can run continuously during operation ofthe autonomous vehicle 10.

The method begins at 402. At 404, the method receives the pitch 318 forthe current position of the vehicle 10, as will be discussed with regardto FIG. 6. At 406, the method receives the roll 322 for the currentposition of the vehicle 10, as will be discussed with regard to FIG. 7.At 408, the method retrieves the areas of interest 330 from the interestdata datastore 307 based on the current position of the vehicle 10 fromthe position data 314. At 410, the method determines the vehiclecoordinate locations for the fields of view 41 a-41 n of the sensingdevices 40 a-40 n based on the pitch 318 and the roll 322. In oneexample, the method adds the Y-axis rotation value for the pitch 318and/or the X-axis rotation value for the roll 322 to the known vehiclecoordinate locations for the fields of view 41 a-41 n retrieved with thesensor characteristics 328 to update the vehicle coordinate locations ofthe fields of view 41 a-41 n based on the pitch 318 and the roll 322.

At 412, the method sets the updated fields of view 41 a-41 n of thesensing devices 40 a-40 n equal to the known geographical coordinatelocations of the areas of interest 330. At 414, the method solves thegeometric equations to determine the actuation angle(s) or an amount ofmovement about the X-axis and/or Y-axis to align the updated fields ofview 41 a-41 n with the areas of interest 330. In various embodiments,the method performs the optimization procedure based on one or more ofthe sensor characteristics and the dynamic characteristics to determinean optimized solution. At 416, the method generates one or more controlsignals to the actuators 49 a-49 n of the sensing devices 40 a-40 n tomove the sensor platform 45 a-45 n, and thus, the sensors 43 a-43 n, thedetermined amount about the X-axis and/or the Y-axis based on thesolution. The method ends at 418.

Referring now to FIG. 6, and with continued reference to FIGS. 1-4, aflowchart illustrates a control method 500 that can be performed by thesensing device field of view control system 100 of FIGS. 1-4 todetermine a pitch of the chassis 12 of the vehicle 10 in accordance withthe present disclosure. In one example, the control method 500 isperformed by the processor 44 of the controller 34. As can beappreciated in light of the disclosure, the order of operation withinthe method is not limited to the sequential execution as illustrated inFIG. 6, but may be performed in one or more varying orders as applicableand in accordance with the present disclosure. In various embodiments,the control method 500 can be scheduled to run based on one or morepredetermined events, and/or can run continuously during operation ofthe autonomous vehicle 10.

The method begins at 502. At 504, the method receives the currentposition of the vehicle 10 (from the position data 314) and receives thedetermined path for the vehicle 10 (from the path data 316). At 506, themethod retrieves the map information 310 from the map datastore 300,which comprises the known roadway pitch and/or incline at the currentposition of the vehicle 10 and for the longitudinal prediction horizon.At 508, based on the map information 310 (or other sources), the methoddetermines the planned pitch 311 of the vehicle 10 at the currentposition of the vehicle 10 and for the longitudinal prediction horizon.

At 510, the method performs a coordinate transformation to transform thegeographical coordinate value of the planned pitch 311 into a vehiclecoordinate value for the current position of the vehicle 10 and for thepositions of the vehicle 10 within the longitudinal prediction horizon.The method stores the planned pitch 311 associated with the currentposition of the vehicle 10 and the planned pitch 311 for the coordinatelocations associated with the longitudinal prediction horizon in thepitch datastore 313. At 512, the method receives and processes thechassis pitch data 320 to determine the substantially real-time pitch ofthe chassis 12 of the vehicle 10. At 514, based on the current positionof the vehicle 10 (from the position data 314), the method queries thepitch datastore 313 and retrieves the planned pitch 311 associated withthe current position of the vehicle 10. The method determines the pitch318 based on the planned pitch 311 and the substantially real-timepitch. In one example, the method subtracts the real-time pitch from theplanned pitch. The method ends at 516.

Referring now to FIG. 7, and with continued reference to FIGS. 1-4, aflowchart illustrates a control method 600 that can be performed by thesensing device field of view control system 100 of FIGS. 1-4 todetermine a roll of the chassis 12 of the vehicle 10 in accordance withthe present disclosure. In one example, the control method 600 isperformed by the processor 44 of the controller 34. As can beappreciated in light of the disclosure, the order of operation withinthe method is not limited to the sequential execution as illustrated inFIG. 7, but may be performed in one or more varying orders as applicableand in accordance with the present disclosure. In various embodiments,the control method 600 can be scheduled to run based on one or morepredetermined events, and/or can run continuously during operation ofthe autonomous vehicle 10.

The method begins at 602. At 604, the method receives the currentposition of the vehicle 10 (from the position data 314) and receives thedetermined path for the vehicle 10 (from the path data 316). At 606, themethod retrieves the map information 312 from the map datastore 300,which comprises the known roadway curvature for the current position ofthe vehicle 10, along with the known curvature along the determined pathof the vehicle 10. At 608, based on the map information 312 (or othersources), the method determines the planned roll 317 of the vehicle 10at the current position of the vehicle 10 and for the lateral predictionhorizon.

At 610, the method performs a coordinate transformation to transform thegeographical coordinate value of the planned roll 317 into a vehiclecoordinate value for the current position of the vehicle 10 and for thepositions of the vehicle 10 within the lateral prediction horizon. Themethod stores the planned roll 317 associated with the current positionof the vehicle 10 and the planned roll 317 for the coordinate locationsassociated with the lateral prediction horizon in the roll datastore319. At 612, method receives and processes the chassis roll data 324 todetermine the substantially real-time roll of the chassis 12 of thevehicle 10. At 614, based on the current position of the vehicle 10(from the position data 314), the method queries the roll datastore 319and retrieves the planned roll 317 associated with the current positionof the vehicle 10. The method determines the roll 322 based on theplanned roll 317 and the substantially real-time roll. In one example,the method subtracts the real-time roll from the planned roll. Themethod ends at 616.

Thus, the sensing device field of view control system 100 controls aposition of the sensing devices 40 a-40 n to maintain the field of view41 a-41 n of the sensing devices 40 a-40 n aligned with the predefinedareas of interest 330. By controlling the position of the sensors 43a-43 n of the sensing devices 40 a-40 n based on the pitch and the rollof the vehicle 10, both planned and unplanned (i.e. as observed by thesensing devices 40 a-40 n), the sensing device field of view controlsystem 100 ensures that the field of view 41 a-41 n maintains alignmentwith the areas of interest 330. For example, if the vehicle 10 istraveling down a substantially straight roadway, and the field of viewof a sensing device is orientated in a direction substantially parallelto the direction of travel, based on a planned pitch of the vehicle 10at or near the current location as increasing in the Y-direction, thesensing device field of view control system 100 moves the sensing deviceabout the Y-axis downward based on the motion of the chassis 12.

While at least one exemplary embodiment has been presented in theforegoing detailed description, it should be appreciated that a vastnumber of variations exist. It should also be appreciated that theexemplary embodiment or exemplary embodiments are only examples, and arenot intended to limit the scope, applicability, or configuration of thedisclosure in any way. Rather, the foregoing detailed description willprovide those skilled in the art with a convenient road map forimplementing the exemplary embodiment or exemplary embodiments. Itshould be understood that various changes can be made in the functionand arrangement of elements without departing from the scope of thedisclosure as set forth in the appended claims and the legal equivalentsthereof.

What is claimed is:
 1. A method for controlling a field of view of asensing device of an autonomous vehicle, comprising: receiving a currentposition of the autonomous vehicle along a determined path; retrievingmap information that includes a pitch and a curvature of a roadway at ornear the current position; determining, by a processor, based on the mapinformation, a planned pitch of the autonomous vehicle at or near thecurrent position; determining, by the processor, based on the mapinformation, a planned roll of the autonomous vehicle at or near thecurrent position; determining, by the processor, based on the plannedpitch and the planned roll of the autonomous vehicle, a location of thefield of view of the sensing device; determining, based on the locationof the field of view and a location of an area of interest at thecurrent position, an amount of movement of the sensing device to alignthe field of view with the area of interest; and generating, by theprocessor, one or more control signals to one or more actuatorsassociated with the sensing device to move the sensing device to alignthe field of view based on the determined amount of movement.
 2. Themethod of claim 1, further comprising: calculating, by the processor,based on the planned roll of the autonomous vehicle, an amount ofmovement about a first axis to align the field of view of the sensingdevice with the area of interest at the current position.
 3. The methodof claim 2, further comprising: calculating, by the processor, based onthe planned pitch of the autonomous vehicle, an amount of movement abouta second axis to align the field of view of the sensing device with thearea of interest at the current position.
 4. The method of claim 3,wherein the generating the one or more control signals to the one ormore actuators associated with the sensing device further comprises:retrieving, by the processor, a geographical coordinate location for thearea of interest associated with the current position of the autonomousvehicle from a datastore; retrieving, by the processor, a vehiclecoordinate location for the field of view of the sensing device;updating, by the processor, the vehicle coordinate location for thefield of view based on the planned pitch and planned roll of theautonomous vehicle; determining, by the processor, based on the updatedvehicle coordinate location for the field of view and the geographicalcoordinate location for the area of interest, an amount of movementabout the first axis and the second axis to align the field of view withthe area of interest; and generating, by the processor, the one or morecontrol signals based on the determining.
 5. The method of claim 1,further comprising: receiving, by the processor, sensor signals thatprovide a substantially real-time roll of a chassis of the autonomousvehicle at the current location of the autonomous vehicle; anddetermining, by the processor, a roll of the autonomous vehicle based onthe planned roll at the current location and the substantially real-timeroll of the chassis.
 6. The method of claim 5, wherein the determiningthe roll of the autonomous vehicle further comprises: transforming, bythe processor, the planned roll into a vehicular coordinate system; andsubtracting, by the processor, the substantially real-time roll of thechassis from the planned roll.
 7. The method of claim 1, furthercomprising: receiving, by the processor, sensor signals that provide asubstantially real-time pitch of a chassis of the autonomous vehicle atthe current location of the autonomous vehicle; and determining, by theprocessor, a pitch of the autonomous vehicle based on the planned pitchand the substantially real-time pitch of the chassis.
 8. The method ofclaim 7, wherein the determining the pitch of the autonomous vehiclefurther comprises: transforming, by the processor, the planned pitchinto a vehicular coordinate system; and subtracting, by the processor,the substantially real-time pitch of the chassis from the planned pitch.9. A system for controlling a field of view of a sensing device of anautonomous vehicle, comprising: a source of a current position of theautonomous vehicle along a determined path; a source of map informationthat includes a pitch and a curvature of a roadway at or near thecurrent position; the sensing device having the field of view, and thefield of view of the sensing device movable by one or more actuatorsassociated with the sensing device; a controller, having a processor,that is configured to: determine, based on the map information, aplanned pitch of the autonomous vehicle at or near the current position;determine, based on the map information, a planned roll of theautonomous vehicle at or near the current position; determine, based onthe planned pitch and the planned roll of the autonomous vehicle, alocation of the field of view of the sensing device; determine, based onthe location of the field of view and a location of an area of interestat the current position, an amount of movement of the sensing device toalign the field of view with the area of interest; and generate one ormore control signals to the one or more actuators associated with thesensing device to move the sensing device to align the field of viewbased on the determined amount of movement.
 10. The system of claim 9,wherein the controller is configured to calculate, based on the plannedroll of the autonomous vehicle, an amount of movement about a first axisto align the field of view of the sensing device with the area ofinterest at or near the current position.
 11. The system of claim 10,wherein the controller is configured to calculate, based on the plannedpitch of the autonomous vehicle, an amount of movement about a secondaxis to align the field of view of the sensing device with the area ofinterest at or near the current position.
 12. The system of claim 11,wherein the controller is configured to generate the one or more controlsignals to the one or more actuators associated with the sensing devicebased on a geographical coordinate location for the area of interestassociated with the current position of the autonomous vehicle that isretrieved from a datastore and a vehicle coordinate location for thefield of view of the sensing device that is retrieved from a seconddatastore, the controller configured to update the vehicle coordinatelocation for the field of view based on the planned pitch and plannedroll of the autonomous vehicle, to determine, based on the updatedvehicle coordinate location for the field of view and the geographicalcoordinate location for the area of interest, an amount of movementabout the first axis and the second axis to align the field of view withthe area of interest, and to generate the one or more control signalsbased on the determination of the amount of movement.
 13. The system ofclaim 9, wherein the controller is configured to receive sensor signalsthat provide a substantially real-time roll of a chassis of theautonomous vehicle at the current location of the autonomous vehicle,and to determine a roll of the autonomous vehicle based on the plannedroll at the current location and the substantially real-time roll of thechassis.
 14. The system of claim 13, wherein the controller isconfigured to determine the roll of the autonomous vehicle based on atransformation of the planned roll into a vehicular coordinate system,and the controller is configured to subtract the substantially real-timeroll of the chassis from the planned roll.
 15. The system of claim 9,wherein the controller is configured to receive sensor signals thatprovide a substantially real-time pitch of a chassis of the autonomousvehicle at the current location of the autonomous vehicle, and todetermine a pitch of the autonomous vehicle based on the planned pitchand the substantially real-time pitch of the chassis.
 16. The system ofclaim 15, wherein the controller is configured to determine the pitch ofthe autonomous vehicle based on a transformation of the planned pitchinto a vehicular coordinate system, and the controller is configured tosubtract the substantially real-time pitch of the chassis from theplanned pitch.
 17. An autonomous vehicle, comprising: a source of acurrent position of the autonomous vehicle along a determined path; asource of map information that includes a pitch and a curvature of aroadway at or near the current position; a sensing device having a fieldof view, and the field of view of the sensing device movable by one ormore actuators associated with the sensing device; a controller, havinga processor, that is configured to: determine based on the mapinformation, a planned pitch of the autonomous vehicle at or near thecurrent position; determine based on the map information, a planned rollof the autonomous vehicle at or near the current position; receivesensor signals that provide a substantially real-time pitch of a chassisof the autonomous vehicle at the current location of the autonomousvehicle; determine a pitch of the autonomous vehicle at the currentposition based on the planned pitch and the substantially real-timepitch; determine, based on the pitch and the planned roll of theautonomous vehicle, a location of the field of view of the sensingdevice; determine, based on the location of the field of view and alocation of an area of interest at the current position, an amount ofmovement of the sensing device to align the field of view with the areaof interest; and generate one or more control signals to the one or moreactuators associated with the sensing device to move the sensing deviceto align the field of view based on the determined amount of movement.18. The autonomous vehicle of claim 17, wherein the controller isconfigured to receive sensor signals that provide a substantiallyreal-time roll of a chassis of the autonomous vehicle at the currentlocation of the autonomous vehicle, to determine a roll of theautonomous vehicle at the current position based on the planned roll andthe substantially real-time roll, and to determine the location of thefield of view of the sensing device based on the pitch and the roll ofthe autonomous vehicle.
 19. The autonomous vehicle of claim 18, whereinthe controller is configured to determine the roll of the autonomousvehicle based on a transformation of the planned roll into a vehicularcoordinate system, and the controller is configured to subtract thesubstantially real-time roll of the chassis from the planned roll. 20.The autonomous vehicle of claim 17, wherein the controller is configuredto determine the pitch of the autonomous vehicle based on atransformation of the planned pitch into a vehicular coordinate system,and the controller is configured to subtract the substantially real-timepitch of the chassis from the planned pitch.